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on Network Economics |
| By: | Federico Forte (Central Bank of Argentina) |
| Abstract: | This paper provides an empirical network analysis of the Argentine interbank money market, commonly known as call market, based on data from the Central Bank of Argentina (BCRA). Its main topological features are described applying graph theory, focusing on the unsecured overnight loans settled from 2003 to 2017. The network, where banks are the nodes and the operations between them represent the links, exhibits low density, as is usual in financial networks, and a higher reciprocity than comparable random graphs. It displays a short average distance and its clustering coefficient remains above that of a random network of equal size. Both indicators show values in line with those reported for other interbank networks around the world. Furthermore, the network is prominently disassortative. Different node centrality measures are computed. It is found that a higher centrality enables a node to settle more convenient bilateral interest rates compared with the average market rate, identifying a statistical and economically significant effect by means of a regression analysis. The degree distributions fit better to a Lognormal distribution than to a Poisson or a Power Law. These results constitute a relevant input for systemic risk assessment and provide solid empirical foundations for future theoretical modelling and shock simulations. |
| Keywords: | network analysis, interbank market, systemic risk |
| JEL: | D85 G21 G28 |
| Date: | 2019–10 |
| URL: | http://d.repec.org/n?u=RePEc:bcr:wpaper:201987&r=all |
| By: | Bora Kim |
| Abstract: | Randomized experiments have become a standard tool in economics. In analyzing randomized experiments, the traditional approach has been based on the Stable Unit Treatment Value (SUTVA: \cite{rubin}) assumption which dictates that there is no interference between individuals. However, the SUTVA assumption fails to hold in many applications due to social interaction, general equilibrium, and/or externality effects. While much progress has been made in relaxing the SUTVA assumption, most of this literature has only considered a setting with perfect compliance to treatment assignment. In practice, however, noncompliance occurs frequently where the actual treatment receipt is different from the assignment to the treatment. In this paper, we study causal effects in randomized experiments with network interference and noncompliance. Spillovers are allowed to occur at both treatment choice stage and outcome realization stage. In particular, we explicitly model treatment choices of agents as a binary game of incomplete information where resulting equilibrium treatment choice probabilities affect outcomes of interest. Outcomes are further characterized by a random coefficient model to allow for general unobserved heterogeneity in the causal effects. After defining our causal parameters of interest, we propose a simple control function estimator and derive its asymptotic properties under large-network asymptotics. We apply our methods to the randomized subsidy program of \cite{dupas} where we find evidence of spillover effects on both short-run and long-run adoption of insecticide-treated bed nets. Finally, we illustrate the usefulness of our methods by analyzing the impact of counterfactual subsidy policies. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.13710&r=all |
| By: | Gabriel Montes Rojas (Instituto Interdisciplinario de Economía Política de Buenos Aires - UBA - CONICET) |
| Abstract: | This paper develops a subgraph network random effects error components for network data regression models. In particular, it allows for edge and triangle specific components, which serve as a basal model for modeling network effects. It then evaluates the potential effects of ignoring network effects in the estimation of the variance-covariance matrix. It also proposes consistent estimator of the variance components and Lagrange Multiplier tests for evaluating the appropriate model of random components in networks. Monte Carlo simulations show that the tests have good performance in finite samples. It applies the proposed tests to the Call interbank market in Argentina. |
| Keywords: | Networks, Clusters, Moulton Factor |
| JEL: | C2 C12 |
| URL: | http://d.repec.org/n?u=RePEc:ake:iiepdt:201944&r=all |
| By: | Tullio Buccellato (Economic Research Department, Confindustria); Riccardo Busin (VERA, University of Venice Cà Foscari); Roberto Casarin (Department of Economics, University of Venice Cà Foscari); Giancarlo Corò (Department of Economics, University of Venice Cà Foscari) |
| Abstract: | This paper contributes to the literature on interlocking directorates (ID) by providing a new solution to the two econometric issues arising in the joint analysis of interlocks and firm performance which are the endogenous nature of ID and sample selection bias due to the exclusion of isolated firms. Some key determinants of ID network formation are identified and used to check for endogeneity. We analyze the impact of the positioning in the network on firms’ performance and inspect how the impact varies across firms of different sizes drawing on information relating to 37,324 firms in the interlocking network which, to our knowledge, is the widest dataset ever used in approaching the study of ID. Our results, made robust for endogeneity and sample selection bias, suggest that eigenvector centrality and the clustering coefficient have a positive and significant impact on all the performance measures and that this effect is more pronounced for small firms. |
| Keywords: | Firm performance, interlocking directorates, network formation, network econometrics |
| JEL: | C02 C26 G30 G34 D85 L14 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:2020:25&r=all |
| By: | Tadao Hoshino |
| Abstract: | In this study, we consider a pairwise network formation model in which each dyad of agents strategically determines the link status between them. Our model allows the agents to have latent group heterogeneity in the propensity of link formation. For the model estimation, we propose a three-step maximum likelihood (ML) method. First, we obtain consistent estimates for the heterogeneity parameters at individual level using the ML estimator. Second, we estimate the latent group structure using the binary segmentation algorithm based on the results obtained from the first step. Finally, based on the estimated group membership, we re-execute the ML estimation. Under certain regularity conditions, we show that the proposed estimator is asymptotically unbiased and distributed as normal at the parametric rate. As an empirical illustration, we focus on the network data of international visa-free travels. The results indicate the presence of significant strategic complementarity and a certain level of degree heterogeneity in the network formation behavior. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.14886&r=all |
| By: | Baruník, Jozef; Bevilacqua, Mattia; Tunaru, Radu |
| Abstract: | This paper introduces forward-looking measures of the network connectedness of fears in the financial system, arising due to the good and bad beliefs of market participants about uncertainty that spreads unequally across a network of banks. We argue that this asymmetric network structure extracted from call and put traded option prices of the main U.S. banks contains valuable information for predicting macroeconomic conditions and economic uncertainty, and it can serve as a tool for forward-looking systemic risk monitoring. |
| Keywords: | ES/K002309/1; ES/R009724/1 |
| JEL: | J1 C1 |
| Date: | 2020–12–11 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:108199&r=all |
| By: | Giacomo De Luca; Thilo R. Huning; Paulo Santos Monteiro |
| Abstract: | We investigate the impact of social media on the 2016 referendum on the United Kingdom membership of the European Union. We leverage 18 million geo-located Twitter messages originating from the UK in the weeks before the referendum. Using electoral wards as unit of observation, we explore how exogenous variation in Twitter exposure affected the vote share in favor of leaving the EU. Our estimates suggest that in electoral wards less exposed to Twitter the percentage who voted to leave the EU was greater. This is confirmed across several specifications and approaches, including two very different IV identification strategies to address the non-randomness of Twitter usage. To interpret our findings, we propose a model of how bounded rational voters learn in social media networks vulnerable to fake news, and we validate the theoretical framework by estimating how Remain and Leave tweets propagated differently on Twitter in the two months leading to the EU referendum. |
| Keywords: | Fake News, Social Networks, Social Media, Brexit |
| JEL: | D72 D83 L82 L86 |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:yor:yorken:21/01&r=all |